code: def __init__(
      self,
      *,
      dim=(64, 128, 256, 512),
      depth=(2, 2, 8, 2),
      window_size=7,
      num_classes=1000,
      tokenize_local_3_conv=False,
      local_patch_size=4,
      use_peg=False,
      attn_dropout=0.0,
      ff_dropout=0.0,
      channels=3,
  ):
      super().__init__()
      dim = cast_tuple(dim, 4)
      depth = cast_tuple(depth, 4)
      assert (
          len(dim) == 4
      ), "dim needs to be a single value or a tuple of length 4"
      assert (
          len(depth) == 4
      ), "depth needs to be a single value or a tuple of length 4"
  
      self.local_patch_size = local_patch_size
  
      region_patch_size = local_patch_size * window_size
      self.region_patch_size = local_patch_size * window_size
  
      init_dim, *_, last_dim = dim
  
      # local and region encoders
  
      if tokenize_local_3_conv:
          self.local_encoder = nn.Sequential(
              nn.Conv2d(3, init_dim, 3, 2, 1),
              nn.LayerNorm(init_dim),
              nn.GELU(),
              nn.Conv2d(init_dim, init_dim, 3, 2, 1),
              nn.LayerNorm(init_dim),
              nn.GELU(),
              nn.Conv2d(init_dim, init_dim, 3, 1, 1),
          )
      else:
          self.local_encoder = nn.Conv2d(3, init_dim, 8, 4, 3)
  
      self.region_encoder = nn.Sequential(
          Rearrange(
              "b c (h p1) (w p2) -> b (c p1 p2) h w",
              p1=region_patch_size,
              p2=region_patch_size,
          ),
          nn.Conv2d((region_patch_size**2) * channels, init_dim, 1),
      )
  
      # layers
  
      current_dim = init_dim
      self.layers = nn.ModuleList([])
  
      for ind, dim, num_layers in zip(range(4), dim, depth):
          not_first = ind != 0
          need_downsample = not_first
          need_peg = not_first and use_peg
  
          self.layers.append(
              nn.ModuleList(
                  [
                      Downsample(current_dim, dim)
                      if need_downsample
                      else nn.Identity(),
                      PEG(dim) if need_peg else nn.Identity(),
                      R2LTransformer(
                          dim,
                          depth=num_layers,
                          window_size=window_size,
                          attn_dropout=attn_dropout,
                          ff_dropout=ff_dropout,
                      ),
                  ]
              )
          )
  
          current_dim = dim
  
      # final logits
  
      self.to_logits = nn.Sequential(
          Reduce("b c h w -> b c", "mean"),
          nn.LayerNorm(last_dim),
          nn.Linear(last_dim, num_classes),
      )
  
imports: import torch
  from torch import nn, einsum
  from einops import rearrange
  from einops.layers.torch import Rearrange, Reduce
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new RegionViT object from inherited base classes: nn with example: obj = RegionViT()